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Figure 1. Protocol phases: a) network scenario b) Discovery phase; c) Maintenance phase - Hello
packets d) QBAR algorithm - switching on an alternative path
of our proposed algorithm can be implemented
on the top of the AODV protocol and do not
require much overhead in terms of memory and
computation.
tion. Channel contention and interference occur
between different sensor flows with, consequently,
an outgoing time variant channel available for
the communication capacity of a sensor node.
The impact of this phenomenon on congestion is
exacerbated by the high density of sensor nodes
in the network topology. Based on the underly-
ing MAC mechanism, after several unsuccessful
retransmissions, these packets are dropped at the
sender node. In addition, the number of nodes
directly affects the reliability of the information.
Although a higher number of transmitting source
patients can improve the accuracy (i.e. in the
ZigBee protocol it can reduce packet collision), it
can degrade the network performance in terms of
packet losses due to the excessive load to the sink.
Finally, the congestion phenomenon is amplified
by the “Many-to-One Nature” of the sensor-sink
cluster: indeed communication from multiple
sensors to a single sink may create a bottleneck
at the sink. Additionally, the presence of mobile
SIMULATION EXPERIMENTS
The WBAN architecture is usually composed of
different sink-sensor clusters that locally monitor/
control the process. Within each cluster, patient
sensors send data to the sink. Due to the memory
restrictions of the sink/sensor nodes and the limited
capacity of the shared wireless medium network,
congestion may be experienced during the network
operation at the sink node that could represent a
bottleneck node. Congestion leads to a waste of the
communication and energy resources of the sensor
nodes while reducing reliability due to excessive
packet losses. Unlike the conventional congestion
phenomenon in wired networks, in WBANs many
factors can lead to the overall network conges-
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